CN102195886A - Service scheduling method on cloud platform - Google Patents
Service scheduling method on cloud platform Download PDFInfo
- Publication number
- CN102195886A CN102195886A CN2011101413883A CN201110141388A CN102195886A CN 102195886 A CN102195886 A CN 102195886A CN 2011101413883 A CN2011101413883 A CN 2011101413883A CN 201110141388 A CN201110141388 A CN 201110141388A CN 102195886 A CN102195886 A CN 102195886A
- Authority
- CN
- China
- Prior art keywords
- node
- cloud platform
- cpu
- service
- disk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Multi Processors (AREA)
Abstract
The invention relates to a service scheduling method on a cloud platform, wherein the scheduling is to allocate the service requests of a user to each node of the cloud platform by a certain manner so as to achieve load balancing and improve the response service speed of the cloud platform. The method described by the invention mainly comprises the following steps: the node of the cloud platform periodically transmits the resource utilization rates of a local computer, such as the CPU utilization rate, the memory utilization rate and the magnetic disc utilization rate, to a scheduler to well reflect the actual load condition of each node of the cloud platform, and uses a weighted least-connection dynamic scheduling method to achieve WSSC (Web Service Scheduling On Cloud) while the resource utilization rate is considered. The method can improve the service response speed of the cloud platform to a great extent.
Description
Technical field
The present invention relates to the Computer Applied Technology field, relate in particular to the service scheduling method in the platform.
Background technology
At present, cloud computing becomes the topic that IT and internet industry are discussed warmly, has also caused the extensive concern of industry.Cloud computing is not a kind of new technology in fact, and is meant a kind of payment and use pattern of new IT infrastructure.Cloud computing is the product that traditional calculations machine technology such as grid computing, Distributed Calculation, parallel computation, effectiveness calculating, the network storage, virtual, load balancing and network technical development merge.It is intended to by network the relatively low computational entity of a plurality of costs is integrated into a perfect system with powerful calculating ability, and by advanced persons' such as SaaS, PaaS, IaaS, MSP business model this powerful computing ability is distributed in terminal use's hand.A core concept of cloud computing is exactly by improving constantly the disposal ability of " cloud ", and then the processing that reduces user terminal is born, finally make user terminal be simplified to a simple input-output equipment, and can enjoy the powerful computing ability of " cloud " as required.
The core concept of cloud computing is computational resource unified management and the scheduling that connects with network in a large number, constitutes a computational resource pond to user or application on-demand service.On-demand service just need be with traditional application deployment in the cloud platform, and provides particular user with service manner, and so, the service request that how to respond the user fast will become particularly important.
Summary of the invention
Related service scheduling method among the present invention has improved the speed of cloud platform response user service request, has guaranteed the load balancingization of each node in the cloud platform simultaneously.
Service scheduling method on a kind of cloud platform provided by the invention comprises that the following step poly-:
1) each node of cloud platform calculates cpu utilance, memory usage, and the disk utilance, and its result sent to scheduler by procotol.
2) according to the resource utilization of each node of cloud platform, scheduler adopts weighting method to calculate the weight of each node of cloud platform.
3) calculate the node that responds current service according to each node weight of cloud platform and linking number.
Wherein, in described step 1), utilize the idle process time to calculate cpu busy percentage divided by CPU total time; Utilize amount of free memory to obtain memory usage divided by total memory size; Utilize idle disk size to obtain the disk utilization rate divided by total disk size.
Wherein, in described step 2) in, the various resource utilizations in the step 1) are weighted on average, calculate the load weighted value of each node on the cloud platform.
Wherein, in described step 3), according to step 2) weighted value that obtains, utilize WLC (Weighted Least-Connection Scheduling) algorithmic dispatching service request.
Description of drawings
Fig. 1 is dispatching method of the present invention (WSSC) flow chart.
Fig. 2 is a WLC dispatching method flow chart.
Embodiment
For making feature of the present invention and advantage obtain clearer understanding,, be described in detail below below in conjunction with accompanying drawing: as shown in Figure 1,
Be example with the cloud platform that (SuSE) Linux OS is housed below, concrete enforcement of the present invention is described.
As shown in Figure 1, Fig. 1 right half part is represented the node in the cloud platform, and Fig. 1 left-half is represented the scheduler in the cloud platform.Node regularly obtains cpu busy percentage, memory usage and the disk utilance of self, and these information are sent to scheduler, after scheduler is received these information, adopt weighting method to calculate the weighted value of the resource operating position of each node, after scheduler is received service request, just according to the resource operating position and the linking number of current each node, adopt the WLC algorithm computation to go out the lightest node of load, and service request is transmitted to this node, allow it reply this service request, upgrade the connection numerical value of this node simultaneously.
Concrete steps are as follows:
Step 1: in linux system, file/proc/stat has preserved operating system from the start various times till now, such as user space program running time, and system attitude running time etc., so we only need to resolve this document and can obtain the CPU time.Code is as follows:
In the above-mentioned code:
User_time represents to begin to be accumulated to current time from system start-up, and the CPU time of user's attitude does not comprise the CPU time of nice value for negative usefulness that process is put.
Nice_time represents to begin to be accumulated to current time from system start-up, and the nice value is the negative shared CPU time of process.
System_time represents to begin to be accumulated to current time from system start-up, the CPU time of kernel mode usefulness that process is put.
Idle_time represents to begin to be accumulated to current time from system start-up, other stand-by period except that the hard disk I0 stand-by period.
Iowait_time represents to begin to be accumulated to current time from system start-up, the hard disk IO stand-by period.，
Irq_time represents to begin to be accumulated to current time from system start-up, hard break period.
Softirq_time represents to begin to be accumulated to current time from system start-up, soft break period.
Above-mentioned every sum is CPU total time.It is cpu busy percentage that the present invention adopts 1-(idle+iowait)/cpu total time.
In like manner, the operating position of having preserved the current internal memory of system in/proc/meminfo only needs to resolve a this document and can obtain the internal memory operating position, and the code that calculates memory usage is as follows:
In the above-mentioned code, total represents total memory size, and free represents the current residual memory size, and the present invention represents memory usage with 1-free/total.
The instrument df that utilizes linux system to carry can obtain the disk operating position, and the code that calculates the disk utilance is as follows:
In the above-mentioned code, " df-v|grep '/' | the 5th row of the row of awk ' { print$5} ' " expression obtain the output of df-v instrument and contain '/', these row are the disk utilance.Above-mentioned cpu utilance, memory usage, disk utilance are sent to scheduler by computer network with standard network protocol UDP, and its idiographic flow is shown in Fig. 1 right half part.
Step 2: in order to follow the simple and high-efficient principle, reduce the required overhead of algorithm itself as far as possible, the present invention analyzed cloud platform node service load properties with and the characteristics of service are provided.In the present invention, extract three parameters of node server: disk utilance Lstorage, cpu busy percentage Lcpu, memory usage Lmemory, these three calculate in step 1.
The current disposal ability of the node in the cloud platform is come out by the calculation of parameter of these three aspects of each server node.Concrete grammar: be weight coefficient ξ of each parameter setting (∑ ξ=1), the pairing ξ of each parameter decides node server service Effect on Performance degree according to parameter.
The present invention supposes existing service server n platform in the cloud platform, certain node server s in the cloud platform
iAt a time the system of t utilizes situation L (s
i),
L(s
i)＝ξ
cpuLcpu+ξ
storageL
storage+ξ
memoryL
memory
i＝0，1，2...，n-1，∑ξ＝1；
Wherein, Lcpu is a cpu busy percentage, and Lstorage is the disk utilance, and Lmemory is a memory usage.
Consider the value condition of L (si): when this each parameter value of server node all was 1, just represented this server node for the running at full capacity situation this moment, and the value of L in this time (si) is 1; When this each load parameter of server node all got 0, this moment, this server was in idle condition, and L (si) is 0, so L (si) ∈ [0,1].
In actual conditions, be not allow CPU or internal memory to reach 100% utilance, so the present invention is provided with corresponding threshold values δ cpu for these three load parameters, δ storage, δ memory.Wherein δ cpu is the threshold values of Lcpu, and δ storage is the threshold values of Lstorage, and δ memory is the threshold values of Lmemory, works as Lcpu, and Lstorage when any one among the Lmemory surpasses threshold values, just can judge that this node server is fully loaded.Fully loaded server node does not participate in scheduling.
The present invention also takes into full account the difference of finishing the disposal ability between the point server, has introduced this notion of intrinsic disposal ability and has quantized node server maximum processing capability.The intrinsic disposal ability of representing the node server in the following description of this paper with parameter Φ.The value of Φ is big more, and the disposal ability of node server is strong more.For example, desirable Φ value is the average execution speed of single-length fixed point instruction.
The current situation of utilizing of system is used after L (si) quantification, and the intrinsic disposal ability Φ of server node that set before basis provides dynamic weights adjustment model:
λ in the above-mentioned formula is the adjustment factor that weights change, and λ is big more, and the variation of L (si) is obvious more to the influence of W.
W gets 0 when other situation occurring, and other situation comprises the situation of node server full load running, or wrong situation (may be the mistake that hardware causes, also might be the mistake that software causes) appears in the node server.W gets 0 server and does not participate in scheduling.
The above-mentioned good server node processing capability in real time situation of having annotated, system's present load of L (si) expression, with the server handling ability inverse ratio that W represents, promptly when the load of server node was heavy more, its ability of handling new task accordingly was just more little.This relationship between expression can be described the processing capability in real time of server node more accurately.Its code is achieved as follows:
In the above-mentioned code, phi represents the intrinsic disposal ability of node, and cpu represents the cpu utilance, and max_cpu represents cpu peak use rate threshold values, and xicpu represents cpu disposal ability weight, and other by that analogy.
Step 3: after in step 2, calculating the current weight W (si) of every node server node, according to the effective linking number C of current user (si), in conjunction with existing WLC algorithm, find out a minimum node server of C (si)/W (si) value, the value of this ratio gained is more little, the load that this node server is described is light more, user's service request that more capable processing is newly arrived.
Suppose node server set S={s
0, s
1..., s
N-1, n is the server total number;
Node server load set L={L (s
0), L (s
1) ..., L (s
N-1), s wherein
i(i=0,1 ..., n-1) be the node server, L (s
i) (i=0,1 ..., n-1) be node server s
iLoad;
W (s
i) expression node server s
iWeights, the expression s
iDisposal ability;
C (s
i) expression node server s
iCurrent effective linking number;
The current effective linking number summation of system:
When new task requests reached, this new request connection can be sent to node server sj, and and if only if, and it meets the following conditions:
, i=0,1 ..., n-1, W (s
j) ≠ 0 and W (s
i) ≠ 0;
Total be exactly WLC picks out percentage that current linking number in the node server accounts for linking number and its weights than reckling, what meet this condition is the minimum load node.
Because multiplication required cpu cycle is less than division, and trouble-free server weights are all greater than zero, when guaranteeing that W (sj) is zero, this server node can invoked prerequisite under, the Rule of judgment of formula 3-3 can be optimized for:
C(sj)*W(si)＞C(si)*W(sj)
Wherein i, j represent nodes different in the cloud platform, and algorithm mainly is divided into two parts: at first the node server from i=0 begins to search for available node server, i.e. W (si)＞0; In the middle of available node server, find out the node server that satisfies above-mentioned formula then, be the node server of the load minimum of WLC gained.Code is achieved as follows:
Above-mentioned code is promptly chosen a lightest node of load in N node, weight represents the weight of each node of calculating in the step 2; Con represents the current linking number of each node.That is to say that we have replaced the method for the calculating weight of existing WLC algorithm with the weighing computation method in the step 2 here.The loading condition of each node in more can actual response cloud platform, thus make service dispatch more reasonable.
Above-described example has been done detailed explanation to the implementation of various piece of the present invention; but specific implementation form of the present invention is not limited thereto; for the those skilled in the art in present technique field, the various conspicuous change of under the situation of spirit that does not deviate from the method for the invention and claim scope it being carried out is all within protection scope of the present invention.
Claims (6)
1. the dispatching method of serving on the cloud platform is characterized in that it may further comprise the steps:
1) each node of cloud platform calculates cpu utilance, memory usage and disk utilance, and its result is sent to scheduler by procotol;
2) according to the resource utilization of each node of cloud platform, scheduler adopts weighting method to calculate the weight of each node of cloud platform;
3) calculate the node that responds current service according to each node weight of cloud platform and linking number.
2. the method for claim 1 is characterized in that: in described step 1), utilize the time of CPU execution idle process to obtain cpu busy percentage divided by CPU total time.
3. the method for claim 1 is characterized in that: in described step 1), utilize amount of free memory to obtain memory usage divided by total memory size.
4. the method for claim 1 is characterized in that: in described step 1), utilize idle disk size to obtain the disk utilization rate divided by total disk size.
5. the method for claim 1 is characterized in that: in described step 2) in, the weighted value of each node in the cpu busy percentage that obtains in the step 1), memory usage, the disk utilance weighted average calculation cloud platform utilized.
6. the method for claim 1, it is characterized in that: in described step 3), according to step 2) weighted value that obtains, utilize WLC (Weighted Least-Connection Scheduling) algorithm computation to go out the lightest node of load, and service request is transmitted to this node, allow it reply this service request, upgrade the connection numerical value of this node simultaneously.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110141388.3A CN102195886B (en) | 2011-05-30 | 2011-05-30 | Service scheduling method on cloud platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110141388.3A CN102195886B (en) | 2011-05-30 | 2011-05-30 | Service scheduling method on cloud platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102195886A true CN102195886A (en) | 2011-09-21 |
| CN102195886B CN102195886B (en) | 2014-02-05 |
Family
ID=44603294
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110141388.3A Active CN102195886B (en) | 2011-05-30 | 2011-05-30 | Service scheduling method on cloud platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102195886B (en) |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102710779A (en) * | 2012-06-06 | 2012-10-03 | 合肥工业大学 | Load balance strategy for allocating service resource based on cloud computing environment |
| CN102801766A (en) * | 2011-11-18 | 2012-11-28 | 北京安天电子设备有限公司 | Method and system for load balancing and data redundancy backup of cloud server |
| CN103024081A (en) * | 2013-01-04 | 2013-04-03 | 福建星网视易信息系统有限公司 | Peer-to-peer communication terminal dispatching method adaptable to time-effect-guaranteed communication systems |
| CN103179048A (en) * | 2011-12-21 | 2013-06-26 | 中国电信股份有限公司 | Method and system for changing main machine quality of service (QoS) strategies of cloud data center |
| CN103179217A (en) * | 2013-04-19 | 2013-06-26 | 中国建设银行股份有限公司 | Load balancing method and device applicable to WEB application server group |
| CN103258149A (en) * | 2012-07-27 | 2013-08-21 | 天津中启创科技有限公司 | Online reading system and method based on cloud computing |
| CN103338228A (en) * | 2013-05-30 | 2013-10-02 | 江苏大学 | Cloud calculating load balancing scheduling algorithm based on double-weighted least-connection algorithm |
| CN103812895A (en) * | 2012-11-12 | 2014-05-21 | 华为技术有限公司 | Scheduling method, management nodes and cloud computing cluster |
| CN104023042A (en) * | 2013-03-01 | 2014-09-03 | 清华大学 | Cloud platform resource scheduling method |
| CN104111875A (en) * | 2014-07-03 | 2014-10-22 | 重庆大学 | Device, system and method for dynamically controlling number of newly-increased tasks at cloud data center |
| CN104182359A (en) * | 2013-05-23 | 2014-12-03 | 杭州宏杉科技有限公司 | Buffer allocation method and device thereof |
| CN104717439A (en) * | 2014-01-02 | 2015-06-17 | 杭州海康威视系统技术有限公司 | Data flow control method and device thereof in video storage system |
| CN104780210A (en) * | 2015-04-13 | 2015-07-15 | 杭州华三通信技术有限公司 | Load balancing method and device |
| CN104852860A (en) * | 2015-05-04 | 2015-08-19 | 四川大学 | Queue-based multi-target scheduling strategy for heterogeneous resources |
| WO2015144089A1 (en) * | 2014-03-28 | 2015-10-01 | Tencent Technology (Shenzhen) Company Limited | Application recommending method and apparatus |
| CN105007337A (en) * | 2015-08-20 | 2015-10-28 | 浪潮(北京)电子信息产业有限公司 | Cluster system load balancing method and system thereof |
| CN105049509A (en) * | 2015-07-23 | 2015-11-11 | 浪潮电子信息产业股份有限公司 | Cluster scheduling method, load balancer and clustering system |
| CN105100237A (en) * | 2015-07-15 | 2015-11-25 | 浪潮(北京)电子信息产业有限公司 | Scheduling control method and scheduling control system |
| CN105302638A (en) * | 2015-11-04 | 2016-02-03 | 国家计算机网络与信息安全管理中心 | MPP (Massively Parallel Processing) cluster task scheduling method based on system load |
| CN105335229A (en) * | 2014-07-25 | 2016-02-17 | 杭州华三通信技术有限公司 | Business resource scheduling method and apparatus |
| CN106612310A (en) * | 2015-10-23 | 2017-05-03 | 腾讯科技（深圳）有限公司 | A server scheduling method, apparatus and system |
| CN106878042A (en) * | 2015-12-18 | 2017-06-20 | 北京奇虎科技有限公司 | Container resource regulating method and system based on SLA |
| CN106911772A (en) * | 2017-02-20 | 2017-06-30 | 联想(北京)有限公司 | Server-assignment method, server-assignment device and electronic equipment |
| CN107247729A (en) * | 2017-05-03 | 2017-10-13 | 中国银联股份有限公司 | A kind of document handling method and device |
| CN107395708A (en) * | 2017-07-14 | 2017-11-24 | 郑州云海信息技术有限公司 | A kind of method and apparatus for handling download request |
| CN108449215A (en) * | 2018-03-31 | 2018-08-24 | 甘肃万维信息技术有限责任公司 | Based on distributed server performance monitoring system |
| CN108551489A (en) * | 2018-05-07 | 2018-09-18 | 广东电网有限责任公司 | A kind of application server load balancing method, system, device and storage medium |
| CN108874535A (en) * | 2018-05-14 | 2018-11-23 | 中国平安人寿保险股份有限公司 | A kind of task adjusting method, computer readable storage medium and terminal device |
| CN109995818A (en) * | 2017-12-29 | 2019-07-09 | 中移（杭州）信息技术有限公司 | A kind of method and device of server load balancing |
| CN110049143A (en) * | 2019-05-31 | 2019-07-23 | 华迪计算机集团有限公司 | Load-balancing method and device |
| US20210400115A1 (en) * | 2016-09-16 | 2021-12-23 | Oracle International Corporation | Cloud operation reservation system |
| CN116112493A (en) * | 2023-02-09 | 2023-05-12 | 网易（杭州）网络有限公司 | Communication method, device, electronic equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101719082A (en) * | 2009-12-24 | 2010-06-02 | 中国科学院计算技术研究所 | Method and system for dispatching application requests in virtual calculation platform |
-
2011
- 2011-05-30 CN CN201110141388.3A patent/CN102195886B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101719082A (en) * | 2009-12-24 | 2010-06-02 | 中国科学院计算技术研究所 | Method and system for dispatching application requests in virtual calculation platform |
Non-Patent Citations (2)
| Title |
|---|
| 《计算机应用》 20071130 龚梅等 "一种集群系统的透明动态反馈负载均衡算法" 第2662-2665页 1-6 , * |
| 龚梅等: ""一种集群系统的透明动态反馈负载均衡算法"", 《计算机应用》, 30 November 2007 (2007-11-30), pages 2662 - 2665 * |
Cited By (52)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102801766B (en) * | 2011-11-18 | 2015-01-07 | 北京安天电子设备有限公司 | Method and system for load balancing and data redundancy backup of cloud server |
| CN102801766A (en) * | 2011-11-18 | 2012-11-28 | 北京安天电子设备有限公司 | Method and system for load balancing and data redundancy backup of cloud server |
| CN103179048A (en) * | 2011-12-21 | 2013-06-26 | 中国电信股份有限公司 | Method and system for changing main machine quality of service (QoS) strategies of cloud data center |
| CN103179048B (en) * | 2011-12-21 | 2016-04-13 | 中国电信股份有限公司 | Main frame qos policy transform method and the system of cloud data center |
| CN102710779A (en) * | 2012-06-06 | 2012-10-03 | 合肥工业大学 | Load balance strategy for allocating service resource based on cloud computing environment |
| CN102710779B (en) * | 2012-06-06 | 2014-09-24 | 合肥工业大学 | Load balance strategy for allocating service resource based on cloud computing environment |
| CN103258149A (en) * | 2012-07-27 | 2013-08-21 | 天津中启创科技有限公司 | Online reading system and method based on cloud computing |
| CN103812895A (en) * | 2012-11-12 | 2014-05-21 | 华为技术有限公司 | Scheduling method, management nodes and cloud computing cluster |
| CN103024081A (en) * | 2013-01-04 | 2013-04-03 | 福建星网视易信息系统有限公司 | Peer-to-peer communication terminal dispatching method adaptable to time-effect-guaranteed communication systems |
| CN103024081B (en) * | 2013-01-04 | 2016-01-20 | 福建星网锐捷通讯股份有限公司 | Be applicable to the terminal scheduling method of the point-to-point communication of effective guarantee communication system |
| CN104023042A (en) * | 2013-03-01 | 2014-09-03 | 清华大学 | Cloud platform resource scheduling method |
| CN104023042B (en) * | 2013-03-01 | 2017-05-24 | 清华大学 | Cloud platform resource scheduling method |
| CN103179217B (en) * | 2013-04-19 | 2016-01-13 | 中国建设银行股份有限公司 | A kind of load-balancing method for WEB application server farm and device |
| CN103179217A (en) * | 2013-04-19 | 2013-06-26 | 中国建设银行股份有限公司 | Load balancing method and device applicable to WEB application server group |
| CN104182359A (en) * | 2013-05-23 | 2014-12-03 | 杭州宏杉科技有限公司 | Buffer allocation method and device thereof |
| CN104182359B (en) * | 2013-05-23 | 2017-11-14 | 杭州宏杉科技股份有限公司 | A kind of cache allocation method and device |
| CN103338228B (en) * | 2013-05-30 | 2016-12-28 | 江苏大学 | Cloud computing load balancing dispatching algorithms based on double weighting Smallest connection algorithms |
| CN103338228A (en) * | 2013-05-30 | 2013-10-02 | 江苏大学 | Cloud calculating load balancing scheduling algorithm based on double-weighted least-connection algorithm |
| CN104717439A (en) * | 2014-01-02 | 2015-06-17 | 杭州海康威视系统技术有限公司 | Data flow control method and device thereof in video storage system |
| CN104717439B (en) * | 2014-01-02 | 2017-12-01 | 杭州海康威视系统技术有限公司 | Data flow control method and its device in Video Storage System |
| US10679132B2 (en) | 2014-03-28 | 2020-06-09 | Tencent Technology (Shenzhen) Company Limited | Application recommending method and apparatus |
| US9953262B2 (en) | 2014-03-28 | 2018-04-24 | Tencent Technology (Shenzhen) Company Limited | Application recommending method and apparatus |
| WO2015144089A1 (en) * | 2014-03-28 | 2015-10-01 | Tencent Technology (Shenzhen) Company Limited | Application recommending method and apparatus |
| CN104111875A (en) * | 2014-07-03 | 2014-10-22 | 重庆大学 | Device, system and method for dynamically controlling number of newly-increased tasks at cloud data center |
| CN104111875B (en) * | 2014-07-03 | 2017-11-28 | 重庆大学 | Cloud data center increases number of tasks device for controlling dynamically, system and method newly |
| CN105335229B (en) * | 2014-07-25 | 2020-07-07 | 新华三技术有限公司 | Scheduling method and device of service resources |
| CN105335229A (en) * | 2014-07-25 | 2016-02-17 | 杭州华三通信技术有限公司 | Business resource scheduling method and apparatus |
| CN104780210A (en) * | 2015-04-13 | 2015-07-15 | 杭州华三通信技术有限公司 | Load balancing method and device |
| CN104780210B (en) * | 2015-04-13 | 2019-01-25 | 新华三技术有限公司 | Load-balancing method and device |
| CN104852860A (en) * | 2015-05-04 | 2015-08-19 | 四川大学 | Queue-based multi-target scheduling strategy for heterogeneous resources |
| CN104852860B (en) * | 2015-05-04 | 2019-04-23 | 四川大学 | A kind of heterogeneous resource Multiobjective Scheduling strategy based on queue |
| CN105100237A (en) * | 2015-07-15 | 2015-11-25 | 浪潮(北京)电子信息产业有限公司 | Scheduling control method and scheduling control system |
| CN105049509A (en) * | 2015-07-23 | 2015-11-11 | 浪潮电子信息产业股份有限公司 | Cluster scheduling method, load balancer and clustering system |
| CN105007337A (en) * | 2015-08-20 | 2015-10-28 | 浪潮(北京)电子信息产业有限公司 | Cluster system load balancing method and system thereof |
| CN106612310A (en) * | 2015-10-23 | 2017-05-03 | 腾讯科技（深圳）有限公司 | A server scheduling method, apparatus and system |
| CN105302638A (en) * | 2015-11-04 | 2016-02-03 | 国家计算机网络与信息安全管理中心 | MPP (Massively Parallel Processing) cluster task scheduling method based on system load |
| CN105302638B (en) * | 2015-11-04 | 2018-11-20 | 国家计算机网络与信息安全管理中心 | MPP cluster task dispatching method based on system load |
| CN106878042A (en) * | 2015-12-18 | 2017-06-20 | 北京奇虎科技有限公司 | Container resource regulating method and system based on SLA |
| US11503128B2 (en) * | 2016-09-16 | 2022-11-15 | Oracle International Corporation | Cloud operation reservation system |
| US20210400115A1 (en) * | 2016-09-16 | 2021-12-23 | Oracle International Corporation | Cloud operation reservation system |
| CN106911772A (en) * | 2017-02-20 | 2017-06-30 | 联想(北京)有限公司 | Server-assignment method, server-assignment device and electronic equipment |
| CN107247729B (en) * | 2017-05-03 | 2021-04-27 | 中国银联股份有限公司 | File processing method and device |
| CN107247729A (en) * | 2017-05-03 | 2017-10-13 | 中国银联股份有限公司 | A kind of document handling method and device |
| CN107395708A (en) * | 2017-07-14 | 2017-11-24 | 郑州云海信息技术有限公司 | A kind of method and apparatus for handling download request |
| CN107395708B (en) * | 2017-07-14 | 2021-04-02 | 郑州云海信息技术有限公司 | Method and device for processing download request |
| CN109995818A (en) * | 2017-12-29 | 2019-07-09 | 中移（杭州）信息技术有限公司 | A kind of method and device of server load balancing |
| CN108449215A (en) * | 2018-03-31 | 2018-08-24 | 甘肃万维信息技术有限责任公司 | Based on distributed server performance monitoring system |
| CN108551489A (en) * | 2018-05-07 | 2018-09-18 | 广东电网有限责任公司 | A kind of application server load balancing method, system, device and storage medium |
| CN108874535A (en) * | 2018-05-14 | 2018-11-23 | 中国平安人寿保险股份有限公司 | A kind of task adjusting method, computer readable storage medium and terminal device |
| CN108874535B (en) * | 2018-05-14 | 2022-06-10 | 中国平安人寿保险股份有限公司 | Task adjusting method, computer readable storage medium and terminal device |
| CN110049143A (en) * | 2019-05-31 | 2019-07-23 | 华迪计算机集团有限公司 | Load-balancing method and device |
| CN116112493A (en) * | 2023-02-09 | 2023-05-12 | 网易（杭州）网络有限公司 | Communication method, device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102195886B (en) | 2014-02-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102195886B (en) | Service scheduling method on cloud platform | |
| CN103713956B (en) | Method for intelligent weighing load balance in cloud computing virtualized management environment | |
| EP2466460B1 (en) | Compiling apparatus and method for a multicore device | |
| EP3129880B1 (en) | Method and device for augmenting and releasing capacity of computing resources in real-time stream computing system | |
| Enokido et al. | Process allocation algorithms for saving power consumption in peer-to-peer systems | |
| CN102111337B (en) | Method and system for task scheduling | |
| CN103338228A (en) | Cloud calculating load balancing scheduling algorithm based on double-weighted least-connection algorithm | |
| Peschlow et al. | A flexible dynamic partitioning algorithm for optimistic distributed simulation | |
| CN102281290B (en) | Emulation system and method for a PaaS (Platform-as-a-service) cloud platform | |
| CN104902001B (en) | Web request load-balancing method based on operating system virtualization | |
| KR20110049507A (en) | Apparatus and method for executing application | |
| Stavrinides et al. | Scheduling real‐time bag‐of‐tasks applications with approximate computations in SaaS clouds | |
| CN105760227B (en) | Resource regulating method and system under cloud environment | |
| Duolikun et al. | An energy-aware algorithm to migrate virtual machines in a server cluster | |
| Stavrinides et al. | Cost‐aware cloud bursting in a fog‐cloud environment with real‐time workflow applications | |
| CN103488538B (en) | Application extension device and application extension method in cloud computing system | |
| CN114706689B (en) | Multi-core processor task scheduling method and system based on subtask characteristics | |
| Duolikun et al. | An energy-efficient process migration approach to reducing electric energy consumption in a cluster of servers | |
| Samir et al. | Autoscaling recovery actions for container‐based clusters | |
| CN104519082B (en) | A kind of expansion method and device of cloud computing | |
| Xue et al. | BOLAS: bipartite-graph oriented locality-aware scheduling for MapReduce tasks | |
| Shahapure et al. | Distance and traffic based virtual machine migration for scalability in cloud computing | |
| CN108268310B (en) | Method and device for determining minimum scheduling granularity | |
| CN112148474B (en) | Loongson big data all-in-one self-adaptive task segmentation method and system for load balancing | |
| Mao et al. | Efficient subtorus processor allocation in a multi-dimensional torus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| ASS | Succession or assignment of patent right |
Owner name: BEIHANG UNIVERSITY Free format text: FORMER OWNER: LAN YUQING Effective date: 20130718 |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| COR | Change of bibliographic data |
Free format text: CORRECT: ADDRESS; FROM: 100084 HAIDIAN, BEIJING TO: 100191 HAIDIAN, BEIJING |
|
| TA01 | Transfer of patent application right |
Effective date of registration: 20130718 Address after: 100191 Haidian District, Xueyuan Road, No. 37, Applicant after: Beihang University Address before: 205, room 2, building 15, building 100084, brown stone garden, Dongmen east gate, Old Summer Palace, Beijing, Haidian District Applicant before: Lan Yuqing |
|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210415 Address after: No.217, 2nd floor, block a, No.51, Kunming Hunan Road, Haidian District, Beijing Patentee after: CHINA STANDARD INTELLIGENT SECURITY INFORMATION TECHNOLOGY Co.,Ltd. Address before: 100191 No. 37, Haidian District, Beijing, Xueyuan Road Patentee before: BEIHANG University |